lets_plot.geom_bar¶
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lets_plot.geom_bar(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, **other_args)¶ Display a bar chart which makes the height of the bar proportional to the number of observed variable values, mapped to x axis.
- Parameters
mapping (FeatureSpec) – Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.
data (dict or DataFrame) – The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.
stat (str, default=’count’) – The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (counts number of points with same x-axis coordinate), ‘bin’ (counts number of points with x-axis coordinate in the same bin), ‘smooth’ (performs smoothing - linear default), ‘density’ (computes and draws kernel density estimate).
position (str or FeatureSpec) – Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.
show_legend (bool, default=True) – False - do not show legend for this layer.
sampling (FeatureSpec) – Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.
tooltips (layer_tooltips) – Result of the call to the layer_tooltips() function. Specifies appearance, style and content.
other_args – Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.
- Returns
Geom object specification.
- Return type
LayerSpec
Note
geom_bar() makes the height of the bar proportional to the number of observed variable values, mapped to x axis. Is intended to use for discrete data. If used for continuous data with stat=’bin’ produces histogram for binned data. geom_bar() handles no group aesthetics.
Computed variables:
..count.. : number of points with same x-axis coordinate.
geom_bar() understands the following aesthetics mappings:
x : x-axis value (this values will produce cases or bins for bars).
y : y-axis value (this value will be used to multiply the case’s or bin’s counts).
alpha : transparency level of a layer. Understands numbers between 0 and 1.
color (colour) : color of a geometry lines. Can be continuous or discrete. For continuous value this will be a color gradient between two colors.
fill : color of geometry filling.
size : lines width. Defines bar line width.
Examples
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import numpy as np from lets_plot import * LetsPlot.setup_html() np.random.seed(42) data = {'x': np.random.randint(10, size=100)} ggplot(data, aes(x='x')) + geom_bar()
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import numpy as np from lets_plot import * LetsPlot.setup_html() np.random.seed(42) n = 10 x = np.arange(n) y = 1 + np.random.randint(5, size=n) ggplot() + \ geom_bar(aes(x='x', y='y', fill='x'), data={'x': x, 'y': y}, \ stat='identity', show_legend=False) + \ scale_fill_discrete()
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import numpy as np from lets_plot import * LetsPlot.setup_html() np.random.seed(42) n = 5000 x = np.random.normal(size=n) c = np.random.choice(list('abcde'), size=n) ggplot({'x': x, 'class': c}, aes(x='x')) + \ geom_bar(aes(group='class', fill='class', color='class'), \ stat='bin', sampling=sampling_pick(n=500), alpha=.3, \ tooltips=layer_tooltips().line('@|@class') .line('count|@..count..'))